Future AI applications require performance, reliability and privacy that the existing, cloud-
dependant system architectures cannot provide. In this article, we study orchestration in the …

Abstract Modern applications of Big Data are transcending from being scalable solutions of
data processing and analysis, to now provide advanced functionalities with the ability to …

Today we create and collect more data than we have in the past. All this data comes from
different sources, including social media platforms, our phones and computers, healthcare …

The designers of a new coordination interface enacting complex workflows have to tackle a
dichotomy: choosing a language-independent or language-dependent approach. Language …

In-situ workflows are a particular class of scientific workflows where different components
(such as simulation, visualization, machine learning, and data analysis) run concurrently. In …

The emergence of the edge computing paradigm has shifted data processing from
centralised infrastructures to heterogeneous and geographically distributed infrastructures …

A computational workflow, also known as workflow, consists of tasks that must be executed
in a specific order to attain a specific goal. Often, in fields such as biology, chemistry …

Scientific workflows drive most modern large-scale science breakthroughs by allowing
scientists to define their computations as a set of jobs executed in a given order based on …

The data modeling process is challenging and involves hypotheses and trials. In the
industry, a workflow has been constructed around data modeling. The offered modernized …

Hadoop Yarn is an open-source cluster manager responsible for resource management and
job scheduling. However, data-driven applications are typically organized into workflows …